This document presents a new iris segmentation method for iris recognition systems. The proposed method uses Canny edge detection and Hough transform to locate the iris boundary after finding the pupil boundary using image gray levels. Experiments on the CASIA iris image database of 756 images show the method can accurately detect the iris boundary in 99.2% of images. This is an improvement over other existing segmentation techniques. The key steps of the proposed method are preprocessing, segmentation using Canny edge detection and Hough transform, normalization using the rubber sheet model, feature encoding with Gabor wavelets, and matching with Hamming distance.

1. Volume 2, No. 5, Sept-Oct 2011
International Journal of Advanced Research in Computer Science
RESEARCH PAPER
Available Online at www.ijarcs.info
© 2010, IJARCS All Rights Reserved 340
ISSN No. 0976-5697
A Robust Segmentation Method for Iris Recognition
Sunil Chawla*
Department of Information Technology,
MMEC, M.M. University,
Mullana, India
true.umang@gmail.com
Ashish Oberoi
Department of Computer Science & Engg
MMEC, M.M. University,
Mullana, India
a_oberoi01@yahoo.com
Abstract: In this paper, a new iris Segmentation method is presented. An iris recognition system acquires a human eye image, segments the iris
region from the rest of the image, normalizes this segmented image and encodes features to get a compact iris template. Performance of all
subsequent stages in an iris recognition system is highly dependent on correct detection of pupil-iris and iris-sclera boundaries in the eye images.
In this paper, we present one such system which finds pupil boundary using image gray levels but uses Canny edge detection and Hough
transform to locate iris boundary. Experiments are done on CASIA database of 756 iris images of 108 different persons with both left and right
eyes images available per person. Experimental evaluation shows that the proposed system is accurate and efficient enough for real life
applications. We are able to detect iris boundary almost 99.20% accurately with the proposed approach.
Keywords: Iris, Segmentation, Hough Transform, Canny, Hamming distance.
I. INTRODUCTION
Traditional methods of human identity verification such
as using keys, certificates, passwords, etc., can hardly meet
the requirements of identity verification and recognition in
the modern society. These methods are either based on what
a person possesses (a physical key, ID card, etc.) or what a
person knows (a secret password, etc.), and have certain
weaknesses. Keys may be lost, ID cards may be forged, and
passwords may be stolen. In recent years, biometric
identification is receiving growing attention from both
academia and industry to overcome the aforementioned
weaknesses. Biometrics can be defined as features used for
recognizing and identifying a person based on his
physiological or behavioral characteristics; and today, it is a
common and reliable way to authenticate the identity of a
living person. The process matches the individual's pattern
or template against the records known by the system. As in
all pattern recognition problems, the key issue is the relation
between interclass and intra-class variability: objects can be
reliably classified only if the variability among different
instances of a given class is less than the variability between
different classes.
Various biometric methods have been marshaled in
support of this challenge. The resulting systems are based on
automated recognition of retinal vasculature, fingerprints,
hand shape, handwritten signature, face, gait and voice.
Universality, uniqueness, permanence, measurability,
noninvasiveness and user friendliness are the most important
factors for evaluating different biometric methods. In
addition, for identification applications requiring a large
database of people’s records, simplicity and efficient
comparison of biometric IDs are necessary. Considering the
above requirements, iris patterns appear as an interesting
alternative for reliable visual recognition of persons when
imaging can be done at distances of less than 1 meter
(without contact) and when there is a need to search very
large databases without incurring any false matches despite
a huge number of possibilities. The pattern of human iris
differs from person to person, even between twins. Since
irises react with high sensitivity to light, causing the iris size
and shape change continuously, counterfeiting based on iris
patterns is extremely difficult. However, the pattern is rich
detailed that it is also difficult to recognize it. The iris
pattern can contain many distinguishing features such as
arching ligaments, furrows, ridges, crypts, rings, corona,
freckles, and a zigzag collarette. Thus, the iris is gifted with
the great advantage that its pattern variability among
different persons is huge. Iris begins to form in the third
month of gestation and the structures creating its pattern are
complete by the eighth month. Features of the iris remain
stable and fixed throughout one’s life. In addition, iris is
protected from the external environment behind the cornea
and the eyelid. These characteristics make iris a unique
alternative for human recognition. Fig. 1 shows the sample
iris image.
Figure. 1 Sample Iris Image
A general iris recognition system is composed of four
steps. First, an image containing the user’s eye is captured
by the system. Then, the image is pre-processed to
normalize for the scale and illumination effects of the iris
and localize the iris in the image. Third, features
representing the iris patterns are extracted. Finally, the
recognition decision is made by means of matching. In
contrast to current feature extraction approaches which are
based on complex mathematical description of the iris
texture, a very simple and novel approach is presented in
this paper to extract features from the highly random iris.
The method is based on the fact that any relation between
subparts of a random texture is a random variable. The
presented method is tested with 756 images of CASIA [9]

2. Sunil Chawla et al, International Journal of Advanced Research in Computer Science, 2 (5), Sept –Oct, 2011,340-343
© 2010, IJARCS All Rights Reserved 341
image database and results of the method shows the
proposed system is accurate and efficient enough for real
life applications.
The paper is organized as follows. Section 2 describes
pervious work on iris recognition methods. Section 3
presents details of the proposed approach. Section 4 gives
experimental results obtained for the CASIA database
version 1.
II. RELATED WORK
The French ophthalmologist Alphonse Bertillon seems to
be the first to propose the use of iris pattern (color) as a
basis for personal identification. In 1981, after considerable
studies on the great variations of human eye iris, Flom and
Safir [7] also suggested the use of iris as the basis for
biometric recognition. In 1987, they approached to computer
scientist J. Daugman of Cambridge University, England to
develop iris identification software and published the first
promising results in 1992 [15]. Later, similar efforts were
reported by Wildes, Boles and Li Ma, whose methods
differed both in the iris feature representation (iris signature)
and pattern matching algorithms. The Wildes’ solution [13]
uses Hough transform for iris localization. He models
eyelids with parabolic curves. A Laplacian pyramid (multi-
scale decomposition) is applied to represent distinctive
spatial characteristics of the human iris. Wildes applied a
modified normalized correlation for the matching process.
The Boles’ prototype [1] is based on a one dimensional
representation of the grey level profiles of the iris followed
by detecting the wavelet transform zero-crossings of the
resulting representation. Li Ma [11] contributed Iris research
progress by extracting Iris features by characterizing key
local variations. The Daugman’s system [3, 4] is
implemented exploiting integro-differential operators to
detect iris inner and outer boundaries. 2-D Gabor filters are
applied to extract unique binary vectors constituting an iris
code. Daugman uses a statistical matcher (logical exclusive
OR operator) which computes the average Hamming
distance between two codes (bit to bit test agreement). In
terms of recognition rates (FAR, FRR), the commercial
success of the patented Daugman’s system speaks in his
favour. Indeed, Daugman’s mathematical algorithms have
been contributing to a patented commercial solution. This
biometric identification platform processes iris recognition
through a specific optical unit that enables non-invasive
acquisition of iris images, and a data processing unit.
III. PROPOSED APPROACH
Preprocessing is the initial stage in an iris recognition
system. The histogram graph is obtained for the input eye
image. Then Histogram Equalization is done to the input
image to enhance the image for further process and it is also
used for adjusting the contrast of the image using the
image’s histogram [10]. Once the preprocessing step is
achieved, Segmentation is performed to separate the iris
from the eye image. The iris inner and outer boundaries are
located by finding the edge image using the canny edge
detector. The advantage of canny edge detector over other
edge detectors is that there is very less probability of not
marking any real edges and marking unwanted edges. It uses
a filter based on the first derivative of a Gaussian; hence the
result is a slightly blurred version of the original image.
Canny edge detector mainly involves three steps such as,
finding the gradient, adjust gamma correction, non-
maximum suppression and the hysteresis thresholding. The
threshold values that have been used are 0.19 and 0.2 (low
and high threshold respectively). Adjust gamma correction
controls the overall brightness of an image i.e. it adjusts the
image gamma value. After the edge directions are known,
non maximum suppression is applied to trace along the edge
in the edge direction and suppress any pixel value that is not
considered to be an edge. Hysteresis thresholding removes
the weak edges below the low threshold. Fig. 2 shows the
preprocessing steps which results in an edge detected image.
(a) (b)
(c)
Figure. 2. (a) Adjust gamma correction, (b) Non-maximum suppression, (c)
Canny edge image
Then Hough transform is employed to deduce the radius
and centre coordinates of the pupil and iris regions [8].
Taking the edge pixels as the centre coordinates xc and yc,
and with the desired radius r, circles are drawn according to
the equation:
xc
2
+ yc
2
= r2
(1)
Iris localization is done by canny edge detector and
curve fitting since iris is the annular part between pupil and
sclera. The boundaries of the iris are modeled by two non-
concentric circles and the eyelids are modeled by two
parabolic curves. Daugman [5] used the following integro-
differential operator to localize the iris:
( )
0 0
0 0
, ,
( , )
, , ( )
2r x y
I x y
max r x y G r ds
r r
σ
π
∂
∗
∂ ∫ (2)
In this expression I(x, y) is an image containing eye. The
integro differential operator searches over the image domain
(x,y) for the maximum in the blurred partial derivative with
respect to increasing radius r of the normalized contour
integral of image along a circular arc ds of radius r and
center coordinates (x0, y0). * denotes convolution and Gσ(r)
is a smoothing function subject to the value of convolution.
By making use of the center and the radius of the iris and the
pupil, we set the Cartesian coordinate system to
dimensionless polar coordinate system. The proposed
algorithm is based on the fact that there is some obvious
difference in the intensity values of iris region and rest of
the eye.
Iris normalization is done to improve the precision of
matching. Thus the segmented iris region is converted to
polar coordinate for further process through Daugman’s
rubber-sheet model. The rubber sheet model accounts for
pupil dilation, imaging distance and non-concentric pupil
displacement.

3. Sunil Chawla et al, International Journal of Advanced Research in Computer Science, 2 (5), Sept –Oct, 2011,340-343
© 2010, IJARCS All Rights Reserved 342
Figure. 3: Output of the Iris Normalization process.
Iris Feature Encoding is done using 2D Gabor Wavelets.
Each isolated iris pattern is then demodulated to extract its
phase information using quadrature 2D Gabor wavelets [2].
It amounts to a patch wise phase quantization of the iris
pattern, by identifying in which quadrant of the complex
plane each resultant phasor lies, when given area of the iris
is projected onto complex-valued 2D Gabor wavelets:
2 2 2 2
0 0 0( ) ( ) / ( ) /
{R ,I } sgn{R ,I } ( , ) .i r
e m e mh I e e e d dω θ φ ρ α θ φ β
ρ φ
ρ φ ρ ρ φ− − − − − −
= ∫∫
(3)
where h{Re, Im} can be regarded as a complex-valued
bit whose, real and imaginary parts are either 1 or 0
depending on the sign of the 2D integral; I(ρ,ϕ) is the raw
iris image in a dimensionless polar coordinate system that is
size- and translation-invariant, and which also corrects for
pupil dilation as explained in a later section; α and β are the
multi-scale 2D wavelet size parameters, spanning an 8-fold
range from 0.15 mm to 1.2 mm on the iris; ω is wavelet
frequency, spanning three octaves in inverse proportion to β;
and (r0,θ0) represent the polar coordinates of each region of
iris for which the phasor coordinates h{Re,Im}are computed.
Feature matching is done using the effective method
called hamming distance, which is used to match the two iris
templates. The Hamming distance gives a measure of how
many bits are the same between two bit patterns. Using the
Hamming distance of two bit patterns, decision can be made
as to whether the two patterns were generated from different
irises or from the same one. In comparing the bit patterns X
and Y, the Hamming distance, HD, is defined as the sum of
disagreeing bits (sum of the exclusive-OR between X and Y)
over N, the total number of bits in the bit pattern.
1
1 N
j j
j
HD X Y
N =
= ⊕∑ (4)
If the hamming distance is 0, then the two templates are
generated from the same iris or from different irises.
IV. EXPERIMENTS AND RESULTS
Figure 4: Original images and corresponding segmented images
We applied our algorithm to all dataset images and
evaluated the accuracy for finding the pupil-iris and iris-
sclera boundaries. The source code was written in
MATLAB. The experiments were performed in MATLAB
7.8 running on 32 – bit Windows 7 Operating System. The
PC was an Intel Core 2 Duo 2.00 GHZ processor with 2GB
RAM. We verified the results manually. Table I summarizes
our results. We compared the accuracy and speed of our
system with the only (to the best of our knowledge) open
source iris recognition implementation available, written by
L. Masek and P. Kovesi [10]. The automatic segmentation
model using Integro-differential equations and Hough
transform proved to be successful.
The CASIA database provided good segmentation, since
those eye images had been taken specifically for iris
recognition research and boundaries of iris pupil and sclera
were clearly distinguished. For the CASIA database, the
Hough transform based segmentation technique managed to
correctly segment the iris region from 658 out of 756 eye
images, which corresponds to a success rate of around 87%
as compared to the Hough transform based segmentation
technique that managed to correctly segment the iris region
from 624 out of 756 eye images, which corresponds to a
success rate of around 83%.
Table 1: Comparison of Different Segmentation Techniques
Method No. of eye
images
Properly
Segmented
Accuracy
Masek and
Kovesi’s
method
756 646 85.5%
Daugman’s
Method
756 658 87%
Hough
Transform
756 624 82.5%
Proposed
Method
756 750 99.2%
V. CONCLUSION AND FUTURE SCOPE
This paper has presented an iris recognition system using
an effective edge detection method. Automatic segmentation
was achieved by canny edge detection through the use of the
Hough transform for localizing the iris and pupil regions,
occluding eyelids. Segmented Iris image is normalized using
Rubber-sheet model proposed by J. Daugman and finally the
two irises were matched using hamming distance. With the
756 eye images that are taken from the CASIA database, we
got the success rate of 99.2% which is best as compared to
other methods compared in the paper. Iris Segmentation and
Normalization are significant parts of an efficient Iris
Recognition system and there is always a scope of
betterment so it is seen to assess the quality of input iris
image before segmentation, to consider the noise factors like
eyelashes, eyelids, specular reflections, spectacles, lenses
etc. in more details so that preventive measures for removal
of their effect on the overall accuracy of the system can be
minimized as a future scope of the current work.
VI. REFERENCES
[1]. Boles, W. and Boashah, B., “A Human Identification
Technique Using Images of the Iris and Wavelet
Transform”, IEEE Trans. on Signal Processing, vol.46,
pp. 1185-1188, April 1998.
[2]. J. G. Daugman. High confidence visual recognition of
persons by a test of statistical independence. IEEE

4. Sunil Chawla et al, International Journal of Advanced Research in Computer Science, 2 (5), Sept –Oct, 2011,340-343
© 2010, IJARCS All Rights Reserved 343
Transactions on Pattern Analysis and Machine
Intelligence, Vol. 25, No. 11, pages 1148–1161,
November 1993.
[3]. Daugman, J., “Biometric Personal Identification
System Based on Iris Analysis”, U.S. Patent No.
5291560, pp. 264-271, March 1994.
[4]. Daugman J and Dowing C., “Epigenetic randomness,
complexity and singularity of human iris patterns”,
Proceedings of the Royal Society, B, 268, Biological
Sciences. pp. 1737-1740, 2001.
[5]. Daugman, J., “The importance of being random:
statistical principles of iris recognition”, Pattern
Recognition 36, (2003) pp. 279-291.
[6]. J. Daugman, “How iris recognition works,” IEEE Trans.
Circuits and Systems for Video Technology, vol. 14, no. 1,
pp. 21-30, Jan. 2004.
[7]. Flom, L. and Safir, A., “Iris Recognition System”, US
Patent 4,641,349, 3 Feb. 1987.
[8]. Gonzalez, R.C. (2002), Woods, R.E, Digital Image
Processing, 2nd ed., Prentice Hall.
[9]. Institute of Automation, Chinese Academy of
Sciences. CASIA iris image database, 2004.
http://www.sinobiometrics.com
[10].Libor Masek and Peter Kovesi, MATLAB Source
Code for a Biometric Identification System Based on
Iris Patterns, The School of Computer Science and
Software Engineering, The University of Western
Australia, 2003,
http://www.csse.uwa.edu.au/_pk/studentprojects/libor/
sourcecode.html.
[11].Li Ma, Tieniu Tan, Fellow, IEEE, Yunhong Wang,
Member, IEEE, and Dexin Zhang, “Efficient Iris
Recognition by characterizing key local variations,”
IEEE Transactions on Image Processing, vol. 13, no. 6,
pages 739-750, June 2004.
[12].Singh S. et.al. “Segmentation Techniques for Iris
Recognition System,” International Journal of
Scientific & Engineering Research, Vol. 2, Issue 4,
April-2011.
[13].Wildes, R., “Iris Recognition: An Emerging Biometric
Technology”, Proceeding of IEEE, vol. 85, no. 9, Sep.
1997.
[14].Y. Zhu, T. Tan, and Y. Wang (2000), “Biometric
Personal Identification Based on Iris Patterns,”
International Conference on Pattern Recognition
(ICPR’00), Vol. 2, p.2801, Sept. 2000.
[15].www.cl.cam.ac.uk/users/jgd1000